---
library_name: transformers
license: mit
base_model: m3rg-iitd/matscibert
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MatSciBERT_ST_DA_1800
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# MatSciBERT_ST_DA_1800

This model is a fine-tuned version of [m3rg-iitd/matscibert](https://huggingface.co/m3rg-iitd/matscibert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1671
- Precision: 0.8613
- Recall: 0.8455
- F1: 0.8533
- Accuracy: 0.9746

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0809        | 1.0   | 1050  | 0.1024          | 0.8455    | 0.8230 | 0.8341 | 0.9719   |
| 0.0424        | 2.0   | 2100  | 0.1023          | 0.8371    | 0.8365 | 0.8368 | 0.9714   |
| 0.025         | 3.0   | 3150  | 0.1397          | 0.8397    | 0.8223 | 0.8309 | 0.9702   |
| 0.0142        | 4.0   | 4200  | 0.1403          | 0.8592    | 0.8379 | 0.8484 | 0.9736   |
| 0.0074        | 5.0   | 5250  | 0.1271          | 0.8727    | 0.8641 | 0.8684 | 0.9771   |
| 0.0047        | 6.0   | 6300  | 0.1430          | 0.8742    | 0.8581 | 0.8661 | 0.9769   |
| 0.0023        | 7.0   | 7350  | 0.1777          | 0.8578    | 0.8310 | 0.8442 | 0.9733   |
| 0.0015        | 8.0   | 8400  | 0.1524          | 0.8637    | 0.8581 | 0.8609 | 0.9760   |
| 0.0013        | 9.0   | 9450  | 0.1683          | 0.8609    | 0.8441 | 0.8524 | 0.9745   |
| 0.0008        | 10.0  | 10500 | 0.1671          | 0.8613    | 0.8455 | 0.8533 | 0.9746   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1